A Spatiotemporal Attention-Based Method for Geo-Referenced Video Coding

This paper focuses on the problem that video-GIS has a huge amount of data, which leads to high transmission resource consumption, and introduced attention calculation model to video GIS coding to optimize the coding method so the compression efficiency will be improved as well. Specifically, on the one hand, it will use optical flow technique to calculate the specific location and motion vector of the movement point set of each frame, and reduce the amount of the movement point set integrate with the features of video GIS, then calculate the mask matrix of the foreground movement, and finally compute the Discrete Cosine Transform (DCT) compression integrate with the mask matrix to realize the optimization of the frame macro block level coding. On the other hand, it will calculate the attention of the video frame using the specific location and the motion vector of the movement point set as the primary indicator, and optimize the coding of the video of frame-level according to the calculation result. By experimental verification, the efficiency of video GIS compression is enhanced by the introduction of the features of the video GIS and the cognitive attention theory.

[1]  Akira Kawanaka,et al.  Reduction of blocking artifacts by adaptive DCT coefficient estimation in block-based video coding , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[2]  Keith Cheverst,et al.  Using and Determining Location in a Context-Sensitive Tour Guide , 2001, Computer.

[3]  J. Blat,et al.  VideoGIS: Segmenting and indexing video based on geographic information , 2002 .

[4]  Emmanuel Stefanakis.  Geographic Hypermedia : Concepts and Systems , 2006 .

[5]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[6]  Richard P. Wildes.  A measure of motion salience for surveillance applications , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[7]  Matthias Jarke,et al.  A Hypermedia Afghan Sites and Monuments Database , 2006 .

[8]  Diego Legros,et al.  Spatio‐Temporal Modeling , 2014 .

[9]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Prashant J. Shenoy,et al.  SEVA: sensor-enhanced video annotation , 2005, MULTIMEDIA '05.

[11]  C. Koch,et al.  Models of bottom-up and top-down visual attention , 2000 .

[12]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[13]  Niki Pissinou,et al.  Spatio-Temporal Modeling in Video and Multimedia Geographic Information Systems , 2001, GeoInformatica.

[14]  Smith Barry,et al.  3rd AGILE Conference on Geographic Information Science , 2000 .

[15]  Xuemin Chen,et al.  Digital Video Transcoding for Transmission and Storage , 2004 .

[16]  Jun Wang,et al.  A Traffic Parameters Extraction Method Using Time-Spatial Image Based on Multicameras , 2013, Int. J. Distributed Sens. Networks.

[17]  Kyoung-Ho Choi,et al.  Generation of video metadata supporting video-GIS integration , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..